/** * llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file * * MIT License * * Copyright (c) 2023-2024 The ggml authors * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #pragma once // // GGML Tensor Library // // This documentation is still a work in progress. // If you wish some specific topics to be covered, feel free to drop a comment: // // https://github.com/ggerganov/whisper.cpp/issues/40 // // ## Overview // // This library implements: // // - a set of tensor operations // - automatic differentiation // - basic optimization algorithms // // The aim of this library is to provide a minimalistic approach for various machine learning tasks. This includes, // but is not limited to, the following: // // - linear regression // - support vector machines // - neural networks // // The library allows the user to define a certain function using the available tensor operations. This function // definition is represented internally via a computation graph. Each tensor operation in the function definition // corresponds to a node in the graph. Having the computation graph defined, the user can choose to compute the // function's value and/or its gradient with respect to the input variables. Optionally, the function can be optimized // using one of the available optimization algorithms. // // For example, here we define the function: f(x) = a*x^2 + b // // { // struct ggml_init_params params = { // .mem_size = 16*1024*1024, // .mem_buffer = NULL, // }; // // // memory allocation happens here // struct ggml_context * ctx = ggml_init(params); // // struct ggml_tensor * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); // // ggml_set_param(ctx, x); // x is an input variable // // struct ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); // struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); // struct ggml_tensor * x2 = ggml_mul(ctx, x, x); // struct ggml_tensor * f = ggml_add(ctx, ggml_mul(ctx, a, x2), b); // // ... // } // // Notice that the function definition above does not involve any actual computation. The computation is performed only // when the user explicitly requests it. For example, to compute the function's value at x = 2.0: // // { // ... // // struct ggml_cgraph * gf = ggml_new_graph(ctx); // ggml_build_forward_expand(gf, f); // // // set the input variable and parameter values // ggml_set_f32(x, 2.0f); // ggml_set_f32(a, 3.0f); // ggml_set_f32(b, 4.0f); // // ggml_graph_compute_with_ctx(ctx, &gf, n_threads); // // printf("f = %f\n", ggml_get_f32_1d(f, 0)); // // ... // } // // The actual computation is performed in the ggml_graph_compute() function. // // The ggml_new_tensor_...() functions create new tensors. They are allocated in the memory buffer provided to the // ggml_init() function. You have to be careful not to exceed the memory buffer size. Therefore, you have to know // in advance how much memory you need for your computation. Alternatively, you can allocate a large enough memory // and after defining the computation graph, call the ggml_used_mem() function to find out how much memory was // actually needed. // // The ggml_set_param() function marks a tensor as an input variable. This is used by the automatic // differentiation and optimization algorithms. // // The described approach allows to define the function graph once and then compute its forward or backward graphs // multiple times. All computations will use the same memory buffer allocated in the ggml_init() function. This way // the user can avoid the memory allocation overhead at runtime. // // The library supports multi-dimensional tensors - up to 4 dimensions. The FP16 and FP32 data types are first class // citizens, but in theory the library can be extended to support FP8 and integer data types. // // Each tensor operation produces a new tensor. Initially the library was envisioned to support only the use of unary // and binary operations. Most of the available operations fall into one of these two categories. With time, it became // clear that the library needs to support more complex operations. The way to support these operations is not clear // yet, but a few examples are demonstrated in the following operations: // // - ggml_permute() // - ggml_conv_1d_1s() // - ggml_conv_1d_2s() // // For each tensor operator, the library implements a forward and backward computation function. The forward function // computes the output tensor value given the input tensor values. The backward function computes the adjoint of the // input tensors given the adjoint of the output tensor. For a detailed explanation of what this means, take a // calculus class, or watch the following video: // // What is Automatic Differentiation? // https://www.youtube.com/watch?v=wG_nF1awSSY // // // ## Tensor data (struct ggml_tensor) // // The tensors are stored in memory via the ggml_tensor struct. The structure provides information about the size of // the tensor, the data type, and the memory buffer where the tensor data is stored. Additionally, it contains // pointers to the "source" tensors - i.e. the tensors that were used to compute the current tensor. For example: // // { // struct ggml_tensor * c = ggml_add(ctx, a, b); // // assert(c->src[0] == a); // assert(c->src[1] == b); // } // // The multi-dimensional tensors are stored in row-major order. The ggml_tensor struct contains fields for the // number of elements in each dimension ("ne") as well as the number of bytes ("nb", a.k.a. stride). This allows // to store tensors that are not contiguous in memory, which is useful for operations such as transposition and // permutation. All tensor operations have to take the stride into account and not assume that the tensor is // contiguous in memory. // // The data of the tensor is accessed via the "data" pointer. For example: // // { // const int nx = 2; // const int ny = 3; // // struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny); // // for (int y = 0; y < ny; y++) { // for (int x = 0; x < nx; x++) { // *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y; // } // } // // ... // } // // Alternatively, there are helper functions, such as ggml_get_f32_1d() and ggml_set_f32_1d() that can be used. // // ## The matrix multiplication operator (ggml_mul_mat) // // TODO // // // ## Multi-threading // // TODO // // // ## Overview of ggml.c // // TODO // // // ## SIMD optimizations // // TODO // // // ## Debugging ggml // // TODO // // #ifdef GGML_SHARED # if defined(_WIN32) && !defined(__MINGW32__) # ifdef GGML_BUILD # define GGML_API __declspec(dllexport) # else # define GGML_API __declspec(dllimport) # endif # else # define GGML_API __attribute__ ((visibility ("default"))) # endif #else # define GGML_API #endif #ifdef GGML_MULTIPLATFORM # if defined(_WIN32) # define GGML_CALL # else # define GGML_CALL __attribute__((__ms_abi__)) # endif #else # define GGML_CALL #endif // TODO: support for clang #ifdef __GNUC__ # define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint))) #elif defined(_MSC_VER) # define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func #else # define GGML_DEPRECATED(func, hint) func #endif #ifndef __GNUC__ # define GGML_ATTRIBUTE_FORMAT(...) #elif defined(__MINGW32__) # define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) #else # define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) #endif #include #include #include #include #define GGML_FILE_MAGIC 0x67676d6c // "ggml" #define GGML_FILE_VERSION 2 #define GGML_QNT_VERSION 2 // bump this on quantization format changes #define GGML_QNT_VERSION_FACTOR 1000 // do not change this #define GGML_MAX_DIMS 4 #define GGML_MAX_PARAMS 2048 #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 10 #define GGML_MAX_N_THREADS 512 #define GGML_MAX_OP_PARAMS 64 #ifndef GGML_MAX_NAME # define GGML_MAX_NAME 64 #endif #define GGML_DEFAULT_N_THREADS 4 #define GGML_DEFAULT_GRAPH_SIZE 2048 #if UINTPTR_MAX == 0xFFFFFFFF #define GGML_MEM_ALIGN 4 #else #define GGML_MEM_ALIGN 16 #endif #define GGML_EXIT_SUCCESS 0 #define GGML_EXIT_ABORTED 1 #define GGML_ROPE_TYPE_NEOX 2 #define GGUF_MAGIC "GGUF" #define GGUF_VERSION 3 #define GGUF_DEFAULT_ALIGNMENT 32 #define GGML_UNUSED(x) (void)(x) #define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1)) #ifndef NDEBUG # define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0) #elif defined(__GNUC__) # define GGML_UNREACHABLE() __builtin_unreachable() #elif defined(_MSC_VER) # define GGML_UNREACHABLE() __assume(0) #else # define GGML_UNREACHABLE() ((void) 0) #endif #ifdef __cplusplus # define GGML_NORETURN [[noreturn]] #elif defined(_MSC_VER) # define GGML_NORETURN __declspec(noreturn) #else # define GGML_NORETURN _Noreturn #endif #define GGML_ABORT(...) ggml_abort(__FILE__, __LINE__, __VA_ARGS__) #define GGML_ASSERT(x) if (!(x)) GGML_ABORT("GGML_ASSERT(%s) failed", #x) // used to copy the number of elements and stride in bytes of tensors into local variables. // main purpose is to reduce code duplication and improve readability. // // example: // // GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); // GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); // #define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \ const type prefix##0 = (pointer)->array[0]; \ GGML_UNUSED(prefix##0); #define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \ GGML_TENSOR_LOCALS_1 (type, prefix, pointer, array) \ const type prefix##1 = (pointer)->array[1]; \ GGML_UNUSED(prefix##1); #define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \ GGML_TENSOR_LOCALS_2 (type, prefix, pointer, array) \ const type prefix##2 = (pointer)->array[2]; \ GGML_UNUSED(prefix##2); #define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \ GGML_TENSOR_LOCALS_3 (type, prefix, pointer, array) \ const type prefix##3 = (pointer)->array[3]; \ GGML_UNUSED(prefix##3); #define GGML_TENSOR_UNARY_OP_LOCALS \ GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \ GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \ GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \ GGML_TENSOR_LOCALS(size_t, nb, dst, nb) #define GGML_TENSOR_BINARY_OP_LOCALS \ GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \ GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \ GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \ GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \ GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \ GGML_TENSOR_LOCALS(size_t, nb, dst, nb) #define GGML_TENSOR_BINARY_OP_LOCALS01 \ GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \ GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \ GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \ GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) #ifdef __cplusplus extern "C" { #endif GGML_NORETURN GGML_ATTRIBUTE_FORMAT(3, 4) GGML_API void ggml_abort(const char * file, int line, const char * fmt, ...); enum ggml_status { GGML_STATUS_ALLOC_FAILED = -2, GGML_STATUS_FAILED = -1, GGML_STATUS_SUCCESS = 0, GGML_STATUS_ABORTED = 1, }; // get ggml_status name string GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status); // ieee 754-2008 half-precision float16 // todo: make this not an integral type typedef uint16_t ggml_fp16_t; GGML_API float ggml_fp16_to_fp32(ggml_fp16_t); GGML_API ggml_fp16_t ggml_fp32_to_fp16(float); GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t *, float *, int64_t); GGML_API void ggml_fp32_to_fp16_row(const float *, ggml_fp16_t *, int64_t); // google brain half-precision bfloat16 typedef struct { uint16_t bits; } ggml_bf16_t; GGML_API ggml_bf16_t ggml_fp32_to_bf16(float); GGML_API float ggml_bf16_to_fp32(ggml_bf16_t); // consider just doing << 16 GGML_API void ggml_bf16_to_fp32_row(const ggml_bf16_t *, float *, int64_t); GGML_API void ggml_fp32_to_bf16_row_ref(const float *, ggml_bf16_t *, int64_t); GGML_API void ggml_fp32_to_bf16_row(const float *, ggml_bf16_t *, int64_t); struct ggml_object; struct ggml_context; struct ggml_cgraph; // NOTE: always add types at the end of the enum to keep backward compatibility enum ggml_type { GGML_TYPE_F32 = 0, GGML_TYPE_F16 = 1, GGML_TYPE_Q4_0 = 2, GGML_TYPE_Q4_1 = 3, // GGML_TYPE_Q4_2 = 4, support has been removed // GGML_TYPE_Q4_3 = 5, support has been removed GGML_TYPE_Q5_0 = 6, GGML_TYPE_Q5_1 = 7, GGML_TYPE_Q8_0 = 8, GGML_TYPE_Q8_1 = 9, GGML_TYPE_Q2_K = 10, GGML_TYPE_Q3_K = 11, GGML_TYPE_Q4_K = 12, GGML_TYPE_Q5_K = 13, GGML_TYPE_Q6_K = 14, GGML_TYPE_Q8_K = 15, GGML_TYPE_IQ2_XXS = 16, GGML_TYPE_IQ2_XS = 17, GGML_TYPE_IQ3_XXS = 18, GGML_TYPE_IQ1_S = 19, GGML_TYPE_IQ4_NL = 20, GGML_TYPE_IQ3_S = 21, GGML_TYPE_IQ2_S = 22, GGML_TYPE_IQ4_XS = 23, GGML_TYPE_I8 = 24, GGML_TYPE_I16 = 25, GGML_TYPE_I32 = 26, GGML_TYPE_I64 = 27, GGML_TYPE_F64 = 28, GGML_TYPE_IQ1_M = 29, GGML_TYPE_BF16 = 30, GGML_TYPE_Q4_0_4_4 = 31, GGML_TYPE_Q4_0_4_8 = 32, GGML_TYPE_Q4_0_8_8 = 33, GGML_TYPE_TQ1_0 = 34, GGML_TYPE_TQ2_0 = 35, GGML_TYPE_COUNT, }; // precision enum ggml_prec { GGML_PREC_DEFAULT, GGML_PREC_F32, }; enum ggml_backend_type { GGML_BACKEND_TYPE_CPU = 0, GGML_BACKEND_TYPE_GPU = 10, GGML_BACKEND_TYPE_GPU_SPLIT = 20, }; // model file types enum ggml_ftype { GGML_FTYPE_UNKNOWN = -1, GGML_FTYPE_ALL_F32 = 0, GGML_FTYPE_MOSTLY_F16 = 1, // except 1d tensors GGML_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors GGML_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16 GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors GGML_FTYPE_MOSTLY_IQ1_S = 18, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors GGML_FTYPE_MOSTLY_IQ1_M = 23, // except 1d tensors GGML_FTYPE_MOSTLY_BF16 = 24, // except 1d tensors GGML_FTYPE_MOSTLY_Q4_0_4_4 = 25, // except 1d tensors GGML_FTYPE_MOSTLY_Q4_0_4_8 = 26, // except 1d tensors GGML_FTYPE_MOSTLY_Q4_0_8_8 = 27, // except 1d tensors }; // available tensor operations: enum ggml_op { GGML_OP_NONE = 0, GGML_OP_DUP, GGML_OP_ADD, GGML_OP_ADD1, GGML_OP_ACC, GGML_OP_SUB, GGML_OP_MUL, GGML_OP_DIV, GGML_OP_SQR, GGML_OP_SQRT, GGML_OP_LOG, GGML_OP_SIN, GGML_OP_COS, GGML_OP_SUM, GGML_OP_SUM_ROWS, GGML_OP_MEAN, GGML_OP_ARGMAX, GGML_OP_REPEAT, GGML_OP_REPEAT_BACK, GGML_OP_CONCAT, GGML_OP_SILU_BACK, GGML_OP_NORM, // normalize GGML_OP_RMS_NORM, GGML_OP_RMS_NORM_BACK, GGML_OP_GROUP_NORM, GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID, GGML_OP_OUT_PROD, GGML_OP_SCALE, GGML_OP_SET, GGML_OP_CPY, GGML_OP_CONT, GGML_OP_RESHAPE, GGML_OP_VIEW, GGML_OP_PERMUTE, GGML_OP_TRANSPOSE, GGML_OP_GET_ROWS, GGML_OP_GET_ROWS_BACK, GGML_OP_DIAG, GGML_OP_DIAG_MASK_INF, GGML_OP_DIAG_MASK_ZERO, GGML_OP_SOFT_MAX, GGML_OP_SOFT_MAX_BACK, GGML_OP_ROPE, GGML_OP_ROPE_BACK, GGML_OP_CLAMP, GGML_OP_CONV_TRANSPOSE_1D, GGML_OP_IM2COL, GGML_OP_IM2COL_BACK, GGML_OP_CONV_TRANSPOSE_2D, GGML_OP_POOL_1D, GGML_OP_POOL_2D, GGML_OP_POOL_2D_BACK, GGML_OP_UPSCALE, // nearest interpolate GGML_OP_PAD, GGML_OP_UNPAD, GGML_OP_ARANGE, GGML_OP_TIMESTEP_EMBEDDING, GGML_OP_ARGSORT, GGML_OP_LEAKY_RELU, GGML_OP_FLASH_ATTN_EXT, GGML_OP_FLASH_ATTN_BACK, GGML_OP_SSM_CONV, GGML_OP_SSM_SCAN, GGML_OP_WIN_PART, GGML_OP_WIN_UNPART, GGML_OP_GET_REL_POS, GGML_OP_ADD_REL_POS, GGML_OP_RWKV_WKV, GGML_OP_UNARY, GGML_OP_MAP_UNARY, GGML_OP_MAP_BINARY, GGML_OP_MAP_CUSTOM1_F32, GGML_OP_MAP_CUSTOM2_F32, GGML_OP_MAP_CUSTOM3_F32, GGML_OP_MAP_CUSTOM1, GGML_OP_MAP_CUSTOM2, GGML_OP_MAP_CUSTOM3, GGML_OP_CROSS_ENTROPY_LOSS, GGML_OP_CROSS_ENTROPY_LOSS_BACK, GGML_OP_OPT_STEP_ADAMW, GGML_OP_COUNT, }; enum ggml_unary_op { GGML_UNARY_OP_ABS, GGML_UNARY_OP_SGN, GGML_UNARY_OP_NEG, GGML_UNARY_OP_STEP, GGML_UNARY_OP_TANH, GGML_UNARY_OP_ELU, GGML_UNARY_OP_RELU, GGML_UNARY_OP_SIGMOID, GGML_UNARY_OP_GELU, GGML_UNARY_OP_GELU_QUICK, GGML_UNARY_OP_SILU, GGML_UNARY_OP_HARDSWISH, GGML_UNARY_OP_HARDSIGMOID, GGML_UNARY_OP_EXP, GGML_UNARY_OP_COUNT, }; enum ggml_object_type { GGML_OBJECT_TYPE_TENSOR, GGML_OBJECT_TYPE_GRAPH, GGML_OBJECT_TYPE_WORK_BUFFER }; enum ggml_log_level { GGML_LOG_LEVEL_NONE = 0, GGML_LOG_LEVEL_INFO = 1, GGML_LOG_LEVEL_WARN = 2, GGML_LOG_LEVEL_ERROR = 3, GGML_LOG_LEVEL_DEBUG = 4, GGML_LOG_LEVEL_CONT = 5, // continue previous log }; // this tensor... enum ggml_tensor_flag { GGML_TENSOR_FLAG_INPUT = 1, // ...is an input for the GGML compute graph GGML_TENSOR_FLAG_OUTPUT = 2, // ...is an output for the GGML compute graph GGML_TENSOR_FLAG_PARAM = 4, // ...contains trainable parameters GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up) }; // n-dimensional tensor struct ggml_tensor { enum ggml_type type; GGML_DEPRECATED(enum ggml_backend_type backend, "use the buffer type to find the storage location of the tensor"); struct ggml_backend_buffer * buffer; int64_t ne[GGML_MAX_DIMS]; // number of elements size_t nb[GGML_MAX_DIMS]; // stride in bytes: // nb[0] = ggml_type_size(type) // nb[1] = nb[0] * (ne[0] / ggml_blck_size(type)) + padding // nb[i] = nb[i-1] * ne[i-1] // compute data enum ggml_op op; // op params - allocated as int32_t for alignment int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; int32_t flags; struct ggml_tensor * grad; struct ggml_tensor * src[GGML_MAX_SRC]; // source tensor and offset for views struct ggml_tensor * view_src; size_t view_offs; void * data; char name[GGML_MAX_NAME]; void * extra; // extra things e.g. for ggml-cuda.cu // char padding[4]; }; static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor); // Abort callback // If not NULL, called before ggml computation // If it returns true, the computation is aborted typedef bool (*ggml_abort_callback)(void * data); // Scheduling priorities enum ggml_sched_priority { GGML_SCHED_PRIO_NORMAL, GGML_SCHED_PRIO_MEDIUM, GGML_SCHED_PRIO_HIGH, GGML_SCHED_PRIO_REALTIME }; // Threadpool params // Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults struct ggml_threadpool_params { bool cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings) int n_threads; // number of threads enum ggml_sched_priority prio; // thread priority uint32_t poll; // polling level (0 - no polling, 100 - aggressive polling) bool strict_cpu; // strict cpu placement bool paused; // start in paused state }; struct ggml_threadpool; // forward declaration, see ggml.c typedef struct ggml_threadpool * ggml_threadpool_t; // the compute plan that needs to be prepared for ggml_graph_compute() // since https://github.com/ggerganov/ggml/issues/287 struct ggml_cplan { size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()` uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()` int n_threads; struct ggml_threadpool * threadpool; // abort ggml_graph_compute when true ggml_abort_callback abort_callback; void * abort_callback_data; }; // scratch buffer struct ggml_scratch { size_t offs; size_t size; void * data; }; struct ggml_init_params { // memory pool size_t mem_size; // bytes void * mem_buffer; // if NULL, memory will be allocated internally bool no_alloc; // don't allocate memory for the tensor data }; // numa strategies enum ggml_numa_strategy { GGML_NUMA_STRATEGY_DISABLED = 0, GGML_NUMA_STRATEGY_DISTRIBUTE = 1, GGML_NUMA_STRATEGY_ISOLATE = 2, GGML_NUMA_STRATEGY_NUMACTL = 3, GGML_NUMA_STRATEGY_MIRROR = 4, GGML_NUMA_STRATEGY_COUNT }; // // GUID // // GUID types typedef uint8_t ggml_guid[16]; typedef ggml_guid * ggml_guid_t; GGML_API bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b); // misc GGML_API void ggml_time_init(void); // call this once at the beginning of the program GGML_API int64_t ggml_time_ms(void); GGML_API int64_t ggml_time_us(void); GGML_API int64_t ggml_cycles(void); GGML_API int64_t ggml_cycles_per_ms(void); // accepts a UTF-8 path, even on Windows GGML_API FILE * ggml_fopen(const char * fname, const char * mode); GGML_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node GGML_API void ggml_print_object (const struct ggml_object * obj); GGML_API void ggml_print_objects(const struct ggml_context * ctx); GGML_API GGML_CALL int64_t ggml_nelements (const struct ggml_tensor * tensor); GGML_API GGML_CALL int64_t ggml_nrows (const struct ggml_tensor * tensor); GGML_API GGML_CALL size_t ggml_nbytes (const struct ggml_tensor * tensor); GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN GGML_API GGML_CALL int64_t ggml_blck_size(enum ggml_type type); GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row GGML_DEPRECATED( GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float "use ggml_row_size() instead"); GGML_API GGML_CALL const char * ggml_type_name(enum ggml_type type); GGML_API GGML_CALL const char * ggml_op_name (enum ggml_op op); GGML_API const char * ggml_op_symbol(enum ggml_op op); GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op); GGML_API GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name GGML_API GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_quantized(enum ggml_type type); // TODO: temporary until model loading of ggml examples is refactored GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype); GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_empty (const struct ggml_tensor * tensor); GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor); GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor); GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor); GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars GGML_API GGML_CALL bool ggml_is_contiguous (const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous() GGML_API GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1 GGML_API GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2 GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1); GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1); GGML_API bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1); // use this to compute the memory overhead of a tensor GGML_API size_t ggml_tensor_overhead(void); GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes); // main GGML_API struct ggml_context * ggml_init(struct ggml_init_params params); GGML_API void ggml_free(struct ggml_context * ctx); GGML_API size_t ggml_used_mem(const struct ggml_context * ctx); GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch); GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx); GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc); GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx); GGML_API size_t ggml_get_mem_size (const struct ggml_context * ctx); GGML_API size_t ggml_get_max_tensor_size(const struct ggml_context * ctx); GGML_API struct ggml_tensor * ggml_new_tensor( struct ggml_context * ctx, enum ggml_type type, int n_dims, const int64_t *ne); GGML_API struct ggml_tensor * ggml_new_tensor_1d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0); GGML_API struct ggml_tensor * ggml_new_tensor_2d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1); GGML_API struct ggml_tensor * ggml_new_tensor_3d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2); GGML_API struct ggml_tensor * ggml_new_tensor_4d( struct ggml_context * ctx, enum ggml_type type, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3); GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value); GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src); GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src); // Context tensor enumeration and lookup GGML_API struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx); GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value); // Converts a flat index into coordinates GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3); GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i); GGML_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value); GGML_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); GGML_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value); GGML_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i); GGML_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value); GGML_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); GGML_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value); GGML_API void * ggml_get_data (const struct ggml_tensor * tensor); GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor); GGML_API GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name); GGML_ATTRIBUTE_FORMAT(2, 3) GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...); // // operations on tensors with backpropagation // GGML_API struct ggml_tensor * ggml_dup( struct ggml_context * ctx, struct ggml_tensor * a); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_dup_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_add( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_add_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_add_cast( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, enum ggml_type type); GGML_API struct ggml_tensor * ggml_add1( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_add1_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // dst = a // view(dst, nb1, nb2, nb3, offset) += b // return dst GGML_API struct ggml_tensor * ggml_acc( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset); GGML_API struct ggml_tensor * ggml_acc_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset); GGML_API struct ggml_tensor * ggml_sub( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_sub_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_mul( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_mul_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_div( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_div_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_sqr( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sqr_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sqrt( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sqrt_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_log( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_log_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sin( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sin_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_cos( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_cos_inplace( struct ggml_context * ctx, struct ggml_tensor * a); // return scalar GGML_API struct ggml_tensor * ggml_sum( struct ggml_context * ctx, struct ggml_tensor * a); // sums along rows, with input shape [a,b,c,d] return shape [1,b,c,d] GGML_API struct ggml_tensor * ggml_sum_rows( struct ggml_context * ctx, struct ggml_tensor * a); // mean along rows GGML_API struct ggml_tensor * ggml_mean( struct ggml_context * ctx, struct ggml_tensor * a); // argmax along rows GGML_API struct ggml_tensor * ggml_argmax( struct ggml_context * ctx, struct ggml_tensor * a); // if a is the same shape as b, and a is not parameter, return a // otherwise, return a new tensor: repeat(a) to fit in b GGML_API struct ggml_tensor * ggml_repeat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // sums repetitions in a into shape of b GGML_API struct ggml_tensor * ggml_repeat_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // concat a and b along dim // used in stable-diffusion GGML_API struct ggml_tensor * ggml_concat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int dim); GGML_API struct ggml_tensor * ggml_abs( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_abs_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sgn( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sgn_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_neg( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_neg_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_step( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_step_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_tanh( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_tanh_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_elu( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_elu_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_relu( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_leaky_relu( struct ggml_context * ctx, struct ggml_tensor * a, float negative_slope, bool inplace); GGML_API struct ggml_tensor * ggml_relu_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sigmoid( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_sigmoid_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_gelu( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_gelu_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_gelu_quick( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_gelu_quick_inplace( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_silu( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_silu_inplace( struct ggml_context * ctx, struct ggml_tensor * a); // a - x // b - dy GGML_API struct ggml_tensor * ggml_silu_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // hardswish(x) = x * relu6(x + 3) / 6 GGML_API struct ggml_tensor * ggml_hardswish( struct ggml_context * ctx, struct ggml_tensor * a); // hardsigmoid(x) = relu6(x + 3) / 6 GGML_API struct ggml_tensor * ggml_hardsigmoid( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_exp( struct ggml_context * ctx, struct ggml_tensor * a); GGML_API struct ggml_tensor * ggml_exp_inplace( struct ggml_context * ctx, struct ggml_tensor * a); // normalize along rows GGML_API struct ggml_tensor * ggml_norm( struct ggml_context * ctx, struct ggml_tensor * a, float eps); GGML_API struct ggml_tensor * ggml_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, float eps); GGML_API struct ggml_tensor * ggml_rms_norm( struct ggml_context * ctx, struct ggml_tensor * a, float eps); GGML_API struct ggml_tensor * ggml_rms_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, float eps); // group normalize along ne0*ne1*n_groups // used in stable-diffusion GGML_API struct ggml_tensor * ggml_group_norm( struct ggml_context * ctx, struct ggml_tensor * a, int n_groups, float eps); GGML_API struct ggml_tensor * ggml_group_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_groups, float eps); // a - x // b - dy GGML_API struct ggml_tensor * ggml_rms_norm_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, float eps); // A: k columns, n rows => [ne03, ne02, n, k] // B: k columns, m rows (i.e. we transpose it internally) => [ne03 * x, ne02 * y, m, k] // result is n columns, m rows => [ne03 * x, ne02 * y, m, n] GGML_API struct ggml_tensor * ggml_mul_mat( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // change the precision of a matrix multiplication // set to GGML_PREC_F32 for higher precision (useful for phi-2) GGML_API void ggml_mul_mat_set_prec( struct ggml_tensor * a, enum ggml_prec prec); // indirect matrix multiplication GGML_API struct ggml_tensor * ggml_mul_mat_id( struct ggml_context * ctx, struct ggml_tensor * as, struct ggml_tensor * b, struct ggml_tensor * ids); // A: m columns, n rows, // B: p columns, n rows, // result is m columns, p rows GGML_API struct ggml_tensor * ggml_out_prod( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // // operations on tensors without backpropagation // GGML_API struct ggml_tensor * ggml_scale( struct ggml_context * ctx, struct ggml_tensor * a, float s); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_scale_inplace( struct ggml_context * ctx, struct ggml_tensor * a, float s); // b -> view(a,offset,nb1,nb2,3), return modified a GGML_API struct ggml_tensor * ggml_set( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset); // in bytes // b -> view(a,offset,nb1,nb2,3), return view(a) GGML_API struct ggml_tensor * ggml_set_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset); // in bytes GGML_API struct ggml_tensor * ggml_set_1d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t offset); // in bytes GGML_API struct ggml_tensor * ggml_set_1d_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t offset); // in bytes // b -> view(a,offset,nb1,nb2,3), return modified a GGML_API struct ggml_tensor * ggml_set_2d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t offset); // in bytes // b -> view(a,offset,nb1,nb2,3), return view(a) GGML_API struct ggml_tensor * ggml_set_2d_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t offset); // in bytes // a -> b, return view(b) GGML_API struct ggml_tensor * ggml_cpy( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_cast( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_type type); // make contiguous GGML_API struct ggml_tensor * ggml_cont( struct ggml_context * ctx, struct ggml_tensor * a); // make contiguous, with new shape GGML_API struct ggml_tensor * ggml_cont_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0); GGML_API struct ggml_tensor * ggml_cont_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1); GGML_API struct ggml_tensor * ggml_cont_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2); GGML_API struct ggml_tensor * ggml_cont_4d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3); // return view(a), b specifies the new shape // TODO: when we start computing gradient, make a copy instead of view GGML_API struct ggml_tensor * ggml_reshape( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // return view(a) // TODO: when we start computing gradient, make a copy instead of view GGML_API struct ggml_tensor * ggml_reshape_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0); GGML_API struct ggml_tensor * ggml_reshape_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1); // return view(a) // TODO: when we start computing gradient, make a copy instead of view GGML_API struct ggml_tensor * ggml_reshape_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2); GGML_API struct ggml_tensor * ggml_reshape_4d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3); // offset in bytes GGML_API struct ggml_tensor * ggml_view_1d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, size_t offset); GGML_API struct ggml_tensor * ggml_view_2d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, size_t nb1, // row stride in bytes size_t offset); GGML_API struct ggml_tensor * ggml_view_3d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, size_t nb1, // row stride in bytes size_t nb2, // slice stride in bytes size_t offset); GGML_API struct ggml_tensor * ggml_view_4d( struct ggml_context * ctx, struct ggml_tensor * a, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3, size_t nb1, // row stride in bytes size_t nb2, // slice stride in bytes size_t nb3, size_t offset); GGML_API struct ggml_tensor * ggml_permute( struct ggml_context * ctx, struct ggml_tensor * a, int axis0, int axis1, int axis2, int axis3); // alias for ggml_permute(ctx, a, 1, 0, 2, 3) GGML_API struct ggml_tensor * ggml_transpose( struct ggml_context * ctx, struct ggml_tensor * a); // supports 3D: a->ne[2] == b->ne[1] GGML_API struct ggml_tensor * ggml_get_rows( struct ggml_context * ctx, struct ggml_tensor * a, // data struct ggml_tensor * b); // row indices GGML_API struct ggml_tensor * ggml_get_rows_back( struct ggml_context * ctx, struct ggml_tensor * a, // gradients of ggml_get_rows result struct ggml_tensor * b, // row indices struct ggml_tensor * c); // data for ggml_get_rows, only used for its shape GGML_API struct ggml_tensor * ggml_diag( struct ggml_context * ctx, struct ggml_tensor * a); // set elements above the diagonal to -INF GGML_API struct ggml_tensor * ggml_diag_mask_inf( struct ggml_context * ctx, struct ggml_tensor * a, int n_past); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_diag_mask_inf_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_past); // set elements above the diagonal to 0 GGML_API struct ggml_tensor * ggml_diag_mask_zero( struct ggml_context * ctx, struct ggml_tensor * a, int n_past); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace( struct ggml_context * ctx, struct ggml_tensor * a, int n_past); GGML_API struct ggml_tensor * ggml_soft_max( struct ggml_context * ctx, struct ggml_tensor * a); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_soft_max_inplace( struct ggml_context * ctx, struct ggml_tensor * a); // fused soft_max(a*scale + mask*(ALiBi slope)) // mask is optional // max_bias = 0.0f for no ALiBi GGML_API struct ggml_tensor * ggml_soft_max_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, float scale, float max_bias); GGML_API struct ggml_tensor * ggml_soft_max_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_soft_max_back_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // rotary position embedding // if (mode & 1) - skip n_past elements (NOT SUPPORTED) // if (mode & GGML_ROPE_TYPE_NEOX) - GPT-NeoX style // // b is an int32 vector with size a->ne[2], it contains the positions GGML_API struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, int mode); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_rope_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, int mode); // custom RoPE // c is freq factors (e.g. phi3-128k), (optional) GGML_API struct ggml_tensor * ggml_rope_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, int n_dims, int mode, int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_rope_ext_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, int n_dims, int mode, int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, int mode, int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow), "use ggml_rope_ext instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, int mode, int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow), "use ggml_rope_ext_inplace instead"); // compute correction dims for YaRN RoPE scaling GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]); // rotary position embedding backward, i.e compute dx from dy // a - dy GGML_API struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, struct ggml_tensor * a, // gradients of ggml_rope result struct ggml_tensor * b, // positions struct ggml_tensor * c, // freq factors int n_dims, int mode, int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow); // clamp // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_clamp( struct ggml_context * ctx, struct ggml_tensor * a, float min, float max); // im2col // converts data into a format that effectively results in a convolution when combined with matrix multiplication GGML_API struct ggml_tensor * ggml_im2col( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel struct ggml_tensor * b, // data int s0, // stride dimension 0 int s1, // stride dimension 1 int p0, // padding dimension 0 int p1, // padding dimension 1 int d0, // dilation dimension 0 int d1, // dilation dimension 1 bool is_2D, enum ggml_type dst_type); GGML_API struct ggml_tensor * ggml_im2col_back( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel struct ggml_tensor * b, // gradient of im2col output int64_t * ne, // shape of im2col input int s0, // stride dimension 0 int s1, // stride dimension 1 int p0, // padding dimension 0 int p1, // padding dimension 1 int d0, // dilation dimension 0 int d1, // dilation dimension 1 bool is_2D); GGML_API struct ggml_tensor * ggml_conv_depthwise_2d( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel struct ggml_tensor * b, // data int s0, // stride dimension 0 int s1, // stride dimension 1 int p0, // padding dimension 0 int p1, // padding dimension 1 int d0, // dilation dimension 0 int d1); // dilation dimension 1 GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel struct ggml_tensor * b, // data int s0, // stride int p0, // padding int d0); // dilation // conv_1d with padding = half // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d) GGML_API struct ggml_tensor* ggml_conv_1d_ph( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel struct ggml_tensor * b, // data int s, // stride int d); // dilation GGML_API struct ggml_tensor * ggml_conv_transpose_1d( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel struct ggml_tensor * b, // data int s0, // stride int p0, // padding int d0); // dilation GGML_API struct ggml_tensor * ggml_conv_2d( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel struct ggml_tensor * b, // data int s0, // stride dimension 0 int s1, // stride dimension 1 int p0, // padding dimension 0 int p1, // padding dimension 1 int d0, // dilation dimension 0 int d1); // dilation dimension 1 // kernel size is a->ne[0] x a->ne[1] // stride is equal to kernel size // padding is zero // example: // a: 16 16 3 768 // b: 1024 1024 3 1 // res: 64 64 768 1 // used in sam GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); // kernel size is a->ne[0] x a->ne[1] // stride is 1 // padding is half // example: // a: 3 3 256 256 // b: 64 64 256 1 // res: 64 64 256 1 // used in sam GGML_API struct ggml_tensor * ggml_conv_2d_s1_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int stride); enum ggml_op_pool { GGML_OP_POOL_MAX, GGML_OP_POOL_AVG, GGML_OP_POOL_COUNT, }; GGML_API struct ggml_tensor * ggml_pool_1d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, int k0, // kernel size int s0, // stride int p0); // padding // the result will have 2*p0 padding for the first dimension // and 2*p1 padding for the second dimension GGML_API struct ggml_tensor * ggml_pool_2d( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_op_pool op, int k0, int k1, int s0, int s1, float p0, float p1); GGML_API struct ggml_tensor * ggml_pool_2d_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * af, // "a"/input used in forward pass enum ggml_op_pool op, int k0, int k1, int s0, int s1, float p0, float p1); // nearest interpolate // multiplies ne0 and ne1 by scale factor // used in stable-diffusion GGML_API struct ggml_tensor * ggml_upscale( struct ggml_context * ctx, struct ggml_tensor * a, int scale_factor); // nearest interpolate // nearest interpolate to specified dimensions // used in tortoise.cpp GGML_API struct ggml_tensor * ggml_upscale_ext( struct ggml_context * ctx, struct ggml_tensor * a, int ne0, int ne1, int ne2, int ne3); // pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0] GGML_API struct ggml_tensor * ggml_pad( struct ggml_context * ctx, struct ggml_tensor * a, int p0, int p1, int p2, int p3); // unpad each dimension: [x, ..., x, y, ..., y] -> [x, ..., x] GGML_API struct ggml_tensor * ggml_unpad( struct ggml_context * ctx, struct ggml_tensor * a, int p0, int p1, int p2, int p3); // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151 // timesteps: [N,] // return: [N, dim] GGML_API struct ggml_tensor * ggml_timestep_embedding( struct ggml_context * ctx, struct ggml_tensor * timesteps, int dim, int max_period); // sort rows enum ggml_sort_order { GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC, }; GGML_API struct ggml_tensor * ggml_argsort( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_sort_order order); GGML_API struct ggml_tensor * ggml_arange( struct ggml_context * ctx, float start, float stop, float step); // top k elements per row GGML_API struct ggml_tensor * ggml_top_k( struct ggml_context * ctx, struct ggml_tensor * a, int k); #define GGML_KQ_MASK_PAD 32 // q: [n_embd, n_batch, n_head, 1] // k: [n_embd, n_kv, n_head_kv, 1] // v: [n_embd, n_kv, n_head_kv, 1] !! not transposed !! // mask: [n_kv, n_batch_pad, 1, 1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !! // res: [n_embd, n_head, n_batch, 1] !! permuted !! GGML_API struct ggml_tensor * ggml_flash_attn_ext( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * mask, float scale, float max_bias, float logit_softcap); GGML_API void ggml_flash_attn_ext_set_prec( struct ggml_tensor * a, enum ggml_prec prec); // TODO: needs to be adapted to ggml_flash_attn_ext GGML_API struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * d, bool masked); GGML_API struct ggml_tensor * ggml_ssm_conv( struct ggml_context * ctx, struct ggml_tensor * sx, struct ggml_tensor * c); GGML_API struct ggml_tensor * ggml_ssm_scan( struct ggml_context * ctx, struct ggml_tensor * s, struct ggml_tensor * x, struct ggml_tensor * dt, struct ggml_tensor * A, struct ggml_tensor * B, struct ggml_tensor * C); // partition into non-overlapping windows with padding if needed // example: // a: 768 64 64 1 // w: 14 // res: 768 14 14 25 // used in sam GGML_API struct ggml_tensor * ggml_win_part( struct ggml_context * ctx, struct ggml_tensor * a, int w); // reverse of ggml_win_part // used in sam GGML_API struct ggml_tensor * ggml_win_unpart( struct ggml_context * ctx, struct ggml_tensor * a, int w0, int h0, int w); GGML_API struct ggml_tensor * ggml_unary( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op); GGML_API struct ggml_tensor * ggml_unary_inplace( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op); // used in sam GGML_API struct ggml_tensor * ggml_get_rel_pos( struct ggml_context * ctx, struct ggml_tensor * a, int qh, int kh); // used in sam GGML_API struct ggml_tensor * ggml_add_rel_pos( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * pw, struct ggml_tensor * ph); GGML_API struct ggml_tensor * ggml_add_rel_pos_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * pw, struct ggml_tensor * ph); GGML_API struct ggml_tensor * ggml_rwkv_wkv( struct ggml_context * ctx, struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * r, struct ggml_tensor * tf, struct ggml_tensor * td, struct ggml_tensor * state); // custom operators typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *); typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *); typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *); typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *); typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32( struct ggml_context * ctx, struct ggml_tensor * a, ggml_unary_op_f32_t fun), "use ggml_map_custom1 instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, ggml_unary_op_f32_t fun), "use ggml_map_custom1_inplace instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, ggml_binary_op_f32_t fun), "use ggml_map_custom2 instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, ggml_binary_op_f32_t fun), "use ggml_map_custom2_inplace instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32( struct ggml_context * ctx, struct ggml_tensor * a, ggml_custom1_op_f32_t fun), "use ggml_map_custom1 instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, ggml_custom1_op_f32_t fun), "use ggml_map_custom1_inplace instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, ggml_custom2_op_f32_t fun), "use ggml_map_custom2 instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, ggml_custom2_op_f32_t fun), "use ggml_map_custom2_inplace instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, ggml_custom3_op_f32_t fun), "use ggml_map_custom3 instead"); GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, ggml_custom3_op_f32_t fun), "use ggml_map_custom3_inplace instead"); // custom operators v2 typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata); typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata); typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata); #define GGML_N_TASKS_MAX (-1) // n_tasks == GGML_N_TASKS_MAX means to use max number of tasks GGML_API struct ggml_tensor * ggml_map_custom1( struct ggml_context * ctx, struct ggml_tensor * a, ggml_custom1_op_t fun, int n_tasks, void * userdata); GGML_API struct ggml_tensor * ggml_map_custom1_inplace( struct ggml_context * ctx, struct ggml_tensor * a, ggml_custom1_op_t fun, int n_tasks, void * userdata); GGML_API struct ggml_tensor * ggml_map_custom2( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, ggml_custom2_op_t fun, int n_tasks, void * userdata); GGML_API struct ggml_tensor * ggml_map_custom2_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, ggml_custom2_op_t fun, int n_tasks, void * userdata); GGML_API struct ggml_tensor * ggml_map_custom3( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, ggml_custom3_op_t fun, int n_tasks, void * userdata); GGML_API struct ggml_tensor * ggml_map_custom3_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_tensor * c, ggml_custom3_op_t fun, int n_tasks, void * userdata); // loss function GGML_API struct ggml_tensor * ggml_cross_entropy_loss( struct ggml_context * ctx, struct ggml_tensor * a, // logits struct ggml_tensor * b); // labels GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back( struct ggml_context * ctx, struct ggml_tensor * a, // logits struct ggml_tensor * b, // labels struct ggml_tensor * c); // gradients of cross_entropy_loss result // AdamW optimizer step // Paper: https://arxiv.org/pdf/1711.05101v3.pdf // PyTorch: https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html GGML_API struct ggml_tensor * ggml_opt_step_adamw( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * grad, float alpha, float beta1, float beta2, float eps, float wd); // weight decay // // automatic differentiation // GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor); GGML_API void ggml_set_loss(struct ggml_tensor * tensor); GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate); GGML_API void ggml_build_opt_adamw( struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, float alpha, float beta1, float beta2, float eps, float wd); // weight decay // graph allocation in a context GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads); GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph); GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst); GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1 GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph); GGML_API int ggml_graph_size (struct ggml_cgraph * cgraph); GGML_API struct ggml_tensor * ggml_graph_node (struct ggml_cgraph * cgraph, int i); // if i < 0, returns nodes[n_nodes + i] GGML_API struct ggml_tensor ** ggml_graph_nodes (struct ggml_cgraph * cgraph); GGML_API int ggml_graph_n_nodes(struct ggml_cgraph * cgraph); GGML_API void ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); GGML_API size_t ggml_graph_overhead(void); GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads); GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads); GGML_API void ggml_threadpool_params_init (struct ggml_threadpool_params * p, int n_threads); GGML_API bool ggml_threadpool_params_match (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1); GGML_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params); GGML_API void ggml_threadpool_free (struct ggml_threadpool * threadpool); GGML_API int ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool); GGML_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool); GGML_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool); // ggml_graph_plan() has to be called before ggml_graph_compute() // when plan.work_size > 0, caller must allocate memory for plan.work_data GGML_API struct ggml_cplan ggml_graph_plan( const struct ggml_cgraph * cgraph, int n_threads, /* = GGML_DEFAULT_N_THREADS */ struct ggml_threadpool * threadpool /* = NULL */ ); GGML_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); // same as ggml_graph_compute() but the work data is allocated as a part of the context // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name); GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname); GGML_API struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval); // print info and performance information for the graph GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph); // dump the graph into a file using the dot format GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename); // build gradient checkpointing backward graph gb for gf using provided checkpoints // gb_tmp will contain original backward graph with rewritten backward process nodes, // but without the second forward pass nodes. GGML_API void ggml_build_backward_gradient_checkpointing( struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, struct ggml_cgraph * gb_tmp, struct ggml_tensor * * checkpoints, int n_checkpoints); // // optimization // // optimization methods enum ggml_opt_type { GGML_OPT_TYPE_ADAM, GGML_OPT_TYPE_LBFGS, }; // linesearch methods enum ggml_linesearch { GGML_LINESEARCH_DEFAULT = 1, GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0, GGML_LINESEARCH_BACKTRACKING_WOLFE = 1, GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2, }; // optimization return values enum ggml_opt_result { GGML_OPT_RESULT_OK = 0, GGML_OPT_RESULT_DID_NOT_CONVERGE, GGML_OPT_RESULT_NO_CONTEXT, GGML_OPT_RESULT_INVALID_WOLFE, GGML_OPT_RESULT_FAIL, GGML_OPT_RESULT_CANCEL, GGML_LINESEARCH_FAIL = -128, GGML_LINESEARCH_MINIMUM_STEP, GGML_LINESEARCH_MAXIMUM_STEP, GGML_LINESEARCH_MAXIMUM_ITERATIONS, GGML_LINESEARCH_INVALID_PARAMETERS, }; typedef void (*ggml_opt_callback)(void * data, int accum_step, float * sched, bool * cancel); typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data); // optimization parameters // // see ggml.c (ggml_opt_default_params) for default values // struct ggml_opt_params { enum ggml_opt_type type; size_t graph_size; int n_threads; // delta-based convergence test // // if past == 0 - disabled // if past > 0: // stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|) // int past; float delta; // maximum number of iterations without improvement // // if 0 - disabled // if > 0: // assume convergence if no cost improvement in this number of iterations // int max_no_improvement; bool print_forward_graph; bool print_backward_graph; int n_gradient_accumulation; // ADAM parameters struct { int n_iter; float sched; // schedule multiplier (fixed, decay or warmup) float decay; // weight decay for AdamW, use 0.0f to disable int decay_min_ndim; // minimum number of tensor dimension to apply weight decay float alpha; // learning rate float beta1; float beta2; float eps; // epsilon for numerical stability float eps_f; // epsilon for convergence test float eps_g; // epsilon for convergence test float gclip; // gradient clipping } adam; // LBFGS parameters struct { int m; // number of corrections to approximate the inv. Hessian int n_iter; int max_linesearch; float eps; // convergence tolerance float ftol; // line search tolerance float wolfe; float min_step; float max_step; enum ggml_linesearch linesearch; } lbfgs; }; struct ggml_opt_context { struct ggml_context * ctx; struct ggml_opt_params params; int iter; int64_t nx; // number of parameter elements bool just_initialized; float loss_before; float loss_after; struct { struct ggml_tensor * g; // current gradient struct ggml_tensor * m; // first moment struct ggml_tensor * v; // second moment struct ggml_tensor * pf; // past function values float fx_best; float fx_prev; int n_no_improvement; } adam; struct { struct ggml_tensor * x; // current parameters struct ggml_tensor * xp; // previous parameters struct ggml_tensor * g; // current gradient struct ggml_tensor * gp; // previous gradient struct ggml_tensor * d; // search direction struct ggml_tensor * pf; // past function values struct ggml_tensor * lmal; // the L-BFGS memory alpha struct ggml_tensor * lmys; // the L-BFGS memory ys struct ggml_tensor * lms; // the L-BFGS memory s struct ggml_tensor * lmy; // the L-BFGS memory y float fx_best; float step; int j; int k; int end; int n_no_improvement; } lbfgs; }; GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type); // optimize the function defined by the tensor f GGML_API enum ggml_opt_result ggml_opt( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f); // initialize optimizer context GGML_API void ggml_opt_init( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_opt_params params, int64_t nx); // continue optimizing the function defined by the tensor f GGML_API enum ggml_opt_result ggml_opt_resume( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_tensor * f); // continue optimizing the function defined by the tensor f GGML_API enum ggml_opt_result ggml_opt_resume_g( struct ggml_context * ctx, struct ggml_opt_context * opt, struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb, ggml_opt_callback callback, void * callback_data); // // tensor flags // GGML_API void ggml_set_input(struct ggml_tensor * tensor); GGML_API void ggml_set_output(struct ggml_tensor * tensor); // // quantization // // - ggml_quantize_init can be called multiple times with the same type // it will only initialize the quantization tables for the first call or after ggml_quantize_free // automatically called by ggml_quantize_chunk for convenience // // - ggml_quantize_free will free any memory allocated by ggml_quantize_init // call this at the end of the program to avoid memory leaks // // note: these are thread-safe // GGML_API void ggml_quantize_init(enum ggml_type type); GGML_API void ggml_quantize_free(void); // some quantization type cannot be used without an importance matrix GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type); // calls ggml_quantize_init internally (i.e. can allocate memory) GGML_API size_t ggml_quantize_chunk( enum ggml_type type, const float * src, void * dst, int64_t start, int64_t nrows, int64_t n_per_row, const float * imatrix); // // gguf // enum gguf_type { GGUF_TYPE_UINT8 = 0, GGUF_TYPE_INT8 = 1, GGUF_TYPE_UINT16 = 2, GGUF_TYPE_INT16 = 3, GGUF_TYPE_UINT32 = 4, GGUF_TYPE_INT32 = 5, GGUF_TYPE_FLOAT32 = 6, GGUF_TYPE_BOOL = 7, GGUF_TYPE_STRING = 8, GGUF_TYPE_ARRAY = 9, GGUF_TYPE_UINT64 = 10, GGUF_TYPE_INT64 = 11, GGUF_TYPE_FLOAT64 = 12, GGUF_TYPE_COUNT, // marks the end of the enum }; struct gguf_context; struct gguf_init_params { bool no_alloc; // if not NULL, create a ggml_context and allocate the tensor data in it struct ggml_context ** ctx; }; GGML_API struct gguf_context * gguf_init_empty(void); GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params); //GGML_API struct gguf_context * gguf_init_from_buffer(..); GGML_API void gguf_free(struct gguf_context * ctx); GGML_API const char * gguf_type_name(enum gguf_type type); GGML_API int gguf_get_version (const struct gguf_context * ctx); GGML_API size_t gguf_get_alignment (const struct gguf_context * ctx); GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx); GGML_API void * gguf_get_data (const struct gguf_context * ctx); GGML_API int gguf_get_n_kv(const struct gguf_context * ctx); GGML_API int gguf_find_key(const struct gguf_context * ctx, const char * key); GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int key_id); GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int key_id); GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id); // will abort if the wrong type is used for the key GGML_API uint8_t gguf_get_val_u8 (const struct gguf_context * ctx, int key_id); GGML_API int8_t gguf_get_val_i8 (const struct gguf_context * ctx, int key_id); GGML_API uint16_t gguf_get_val_u16 (const struct gguf_context * ctx, int key_id); GGML_API int16_t gguf_get_val_i16 (const struct gguf_context * ctx, int key_id); GGML_API uint32_t gguf_get_val_u32 (const struct gguf_context * ctx, int key_id); GGML_API int32_t gguf_get_val_i32 (const struct gguf_context * ctx, int key_id); GGML_API float gguf_get_val_f32 (const struct gguf_context * ctx, int key_id); GGML_API uint64_t gguf_get_val_u64 (const struct gguf_context * ctx, int key_id); GGML_API int64_t gguf_get_val_i64 (const struct gguf_context * ctx, int key_id); GGML_API double gguf_get_val_f64 (const struct gguf_context * ctx, int key_id); GGML_API bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id); GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int key_id); GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id); GGML_API int gguf_get_arr_n (const struct gguf_context * ctx, int key_id); GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id); GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i); GGML_API int gguf_get_n_tensors (const struct gguf_context * ctx); GGML_API int gguf_find_tensor (const struct gguf_context * ctx, const char * name); GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i); GGML_API char * gguf_get_tensor_name (const struct gguf_context * ctx, int i); GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int i); // removes key if it exists GGML_API void gguf_remove_key(struct gguf_context * ctx, const char * key); // overrides existing values or adds a new one GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val); GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val); GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val); GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t val); GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val); GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val); GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val); GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val); GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t val); GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double val); GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val); GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val); GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n); GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n); // set or add KV pairs from another context GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src); // manage tensor info GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor); GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type); GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size); // writing gguf files can be done in 2 ways: // // - write the entire gguf_context to a binary file in a single pass: // // gguf_write_to_file(ctx, fname); // // - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data: // // FILE * f = fopen(fname, "wb"); // fseek(f, gguf_get_meta_size(ctx), SEEK_SET); // fwrite(f, ...); // void * data = gguf_meta_get_meta_data(ctx); // fseek(f, 0, SEEK_SET); // fwrite(f, data, gguf_get_meta_size(ctx)); // free(data); // fclose(f); // // write the entire context to a binary file GGML_API void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta); // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx); GGML_API void gguf_get_meta_data(const struct gguf_context * ctx, void * data); // // system info // GGML_API int ggml_cpu_has_avx (void); GGML_API int ggml_cpu_has_avx_vnni (void); GGML_API int ggml_cpu_has_avx2 (void); GGML_API int ggml_cpu_has_avx512 (void); GGML_API int ggml_cpu_has_avx512_vbmi(void); GGML_API int ggml_cpu_has_avx512_vnni(void); GGML_API int ggml_cpu_has_avx512_bf16(void); GGML_API int ggml_cpu_has_fma (void); GGML_API int ggml_cpu_has_neon (void); GGML_API int ggml_cpu_has_sve (void); GGML_API int ggml_cpu_has_arm_fma (void); GGML_API int ggml_cpu_has_metal (void); GGML_API int ggml_cpu_has_f16c (void); GGML_API int ggml_cpu_has_fp16_va (void); GGML_API int ggml_cpu_has_wasm_simd (void); GGML_API int ggml_cpu_has_blas (void); GGML_API int ggml_cpu_has_cuda (void); GGML_API int ggml_cpu_has_vulkan (void); GGML_API int ggml_cpu_has_kompute (void); GGML_API int ggml_cpu_has_gpublas (void); GGML_API int ggml_cpu_has_sse3 (void); GGML_API int ggml_cpu_has_ssse3 (void); GGML_API int ggml_cpu_has_riscv_v (void); GGML_API int ggml_cpu_has_sycl (void); GGML_API int ggml_cpu_has_rpc (void); GGML_API int ggml_cpu_has_vsx (void); GGML_API int ggml_cpu_has_matmul_int8(void); GGML_API int ggml_cpu_has_cann (void); GGML_API int ggml_cpu_has_llamafile (void); // get the sve vector length in bytes GGML_API int ggml_cpu_get_sve_cnt(void); // // Internal types and functions exposed for tests and benchmarks // #ifdef __cplusplus // restrict not standard in C++ #define GGML_RESTRICT #else #define GGML_RESTRICT restrict #endif typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); typedef void (*ggml_from_float_to_mat_t) (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs); typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx, const void * GGML_RESTRICT y, size_t by, int nrc); typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y, int nr, int nc); typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y, int nr, int nc); typedef struct { const char * type_name; int64_t blck_size; int64_t blck_size_interleave; // interleave elements in blocks size_t type_size; bool is_quantized; ggml_to_float_t to_float; ggml_from_float_t from_float; ggml_from_float_t from_float_ref; ggml_from_float_to_mat_t from_float_to_mat; ggml_vec_dot_t vec_dot; enum ggml_type vec_dot_type; int64_t nrows; // number of rows to process simultaneously int64_t ncols; // number of columns to process simultaneously ggml_gemv_t gemv; ggml_gemm_t gemm; } ggml_type_traits_t; GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type); #ifdef __cplusplus } #endif